Amino Acid Pairing for De Novo Design of Self‐Assembling Peptides and Their Drug Delivery Potential
Bibliographic record
Abstract
Abstract Molecular self‐assembly has emerged as the “bottom‐up” engineering route to fabricate functional supramolecules for diverse applications. The design of molecular building units becomes critical in determining the structure, properties, and function of the resulting assemblies. Here, a de novo design principle of amino acid pairing (AAP) to generate new classes of self‐assembling peptides (SAPs) is presented. In this study, the AAP focuses on hydrogen bonding, and ionic and hydrophobic interactions among amino acid pairs. With solely hydrogen bond pairs, SAPs can be constructed with only two amino acids. With all three AAP strategies (hydrogen bonds, ionic and hydrophobic pairs), a short novel SAP is constructed. This peptide can self‐assemble into β‐sheet‐rich nanofibers with a relatively low “critical aggregation concentration (CAC)” of ∼10 μM. It also shows the ability to stabilize and deliver the hydrophobic anticancer agent ellipticine in aqueous solution. The peptide‐drug complexes/co‐assemblies exhibit anticancer activity against human lung carcinoma cells A549 and breast cancer cells MCF‐7, and have good dilution stability. The presented AAP design provides a new strategy to fabricate functional supramolecules with potential applications in nanomedicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".